Comparison of Numerical and Experimental Data in Multi-objective Optimization of a Thermoplastic Molded Part

2013 ◽  
Vol 28 (1) ◽  
pp. 84-106 ◽  
Author(s):  
M. Natalini ◽  
M. Sasso ◽  
D. Amodio
Keyword(s):  
Experimental Data ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Molded Part

scholarly journals Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

2019 ◽  
Author(s):  
Elisabetta Iavarone ◽  
Jane Yi ◽  
Ying Shi ◽  
Bas-Jan Zandt ◽  
Christian O’Reilly ◽  
...  
Keyword(s):  
Experimental Data ◽  
Action Potentials ◽  
Dendritic Structure ◽  
Cellular Level ◽  
Multi Objective Optimization ◽  
Tonic Firing ◽  
Multi Objective ◽  
Wide Range ◽  
Low Threshold ◽  
Thalamocortical System

AbstractSomatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.Author summaryThalamocortical neurons are one of the main components of the thalamocortical system, which are implicated in key functions including sensory transmission and the transition between brain states. These functions are reflected at the cellular level by the ability to generate action potentials in two distinct modes, called burst and tonic firing. Biophysically-detailed computational modeling of these cells can provide a tool to understand the role of these neurons within thalamocortical circuitry. We started by collecting single cell experimental data by applying standardized experimental procedures in brain slices of the rat. Prior work has demonstrated that biological constraints can be integrated using multi-objective optimization to build biologically realistic models of neuron. Here, we employ similar techniques as those previously employed, but extend them to capture the multiple firing modes of thalamic neurons. We compared the model results with additional experimental data test their generalization and quantitatively reject those that deviated significantly from the experimental variability. These models can be readily integrated in a data-driven pipeline to reconstruct and simulate circuit activity in the thalamocortical system.

A Simple GA Based Approach for Multi-Objective Optimization of Machining Parameters

2014 ◽  
Vol 974 ◽  
pp. 402-407 ◽  
Author(s):  
Akhtar Waseem ◽  
Jian Fei Su ◽  
Wu Yi Chen ◽  
Peng Fei Sun
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Simple Approach ◽  
Machining Parameters ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective

A simple approach to multi-objective optimization of machining parameters is presented. Regression analysis of experimental data is carried out to obtain the correlation between cutting parameters and response variables. Finally, Genetic Algorithm (GA) toolbox ofMATLABis used to carry out multi-objective optimization of two objective functions (surface roughness “Ra” & material removal rate “MRR”). Genetic algorithm is found to be a powerful tool for multi-objective optimization of machining parameters in this study.

A multi-objective optimization algorithm for Bouc–Wen–Baber–Noori model to identify reinforced concrete columns failing in different modes

2021 ◽  
pp. 146442072110200
Author(s):  
Zele Li ◽  
Mohammad Noori ◽  
Ying Zhao ◽  
Chunfeng Wan ◽  
Decheng Feng ◽  
...  
Keyword(s):  
Experimental Data ◽  
Reinforced Concrete ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Concrete Columns ◽  
Multi Objective Optimization ◽  
Hysteresis Model ◽  
Restoring Force ◽  
Rc Columns ◽  
Multi Objective

Concrete columns are the most important load-bearing components in civil structures. The potential damage in reinforced concrete (RC) columns could be categorized into three different failure modes: flexural shear (FS) failure, flexural-failure (FF), and shear failure (SF). The corresponding hysteresis loops for each mode differ significantly. Therefore, a multi-parameter hysteretic restoring force model is needed to describe the hysteretic energy dissipation phenomenon and behavior. Identification of the optimal parameter values of a multi-parameter hysteresis model of RC columns under different failure modes is essential in the evaluation of structural inelastic seismic performance. In this paper, a multi-objective optimization algorithm called NSGA-II is employed to identify the parameters of Bouc–Wen–Baber–Noori model (BWBN) hysteresis model, this model has been used for describing the response and modelling restoring force behavior in several structural and mechanical engineering systems, that can fully describe the hysteretic restoring force characteristics of RC columns. An objective function for the restoring force is proposed to identify the parameters of BWBN model. In order to ensure the accuracy of identification, based on the sensitivity analysis, the parameters distribution law of RC columns in different failure modes is obtained. Furthermore, the reference values under different failure modes are proposed. The results presented in this paper will significantly reduce the calculation of subsequent identification. Twelve groups of experimental data are randomly selected to verify the feasibility of the above algorithm. It is demonstrated that using the multi-objective optimization algorithm leads to better identification accuracy with minimum prior experience. Performance of the algorithm is verified using simulated and experimental data. The experimental data of the RC columns were collected from the database of the Pacific Earthquake Engineering Research Center.

scholarly journals Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Amir M. Aboutaleb ◽  
Mark A. Tschopp ◽  
Prahalad K. Rao ◽  
Linkan Bian
Keyword(s):  
Experimental Data ◽  
Additive Manufacturing ◽  
Process Optimization ◽  
Process Parameters ◽  
Process Conditions ◽  
Optimization Approach ◽  
Geometric Accuracy ◽  
Multi Objective Optimization ◽  
Fused Filament Fabrication ◽  
Multi Objective

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Ernestas Filatovas ◽  
Olga Kurasova ◽  
Karthik Sindhya
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Multi-objective Optimization Method for Distribution System Configuration using Pareto Optimal Solution

2008 ◽  
Vol 128 (10) ◽  
pp. 1208-1216 ◽  
Author(s):  
Yasuhiro Hayashi ◽  
Hirotaka Takano ◽  
Junya Matsuki ◽  
Yuji Nishikawa
Keyword(s):  
Distribution System ◽  
Optimal Solution ◽  
Optimization Method ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
System Configuration ◽  
Multi Objective
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